1) Field of the Invention
The invention relates generally to information retrieval systems, more particularly, a system, a method and a computer readable medium for segmenting information.
2) Discussion of the Related Art
Millions of people currently access the Internet on a daily basis to search and shop for goods and services, find information, and to communicate with friends, family, and others who may or may not have like-minded interests. When a consumer wishes to purchase a product or find information on the Internet, the consumer enters an address into a web browser of a website associated with the product, service or information. If the consumer is not aware of a website associated with the product or service, the consumer may search using a search engine.
Search engines are a powerful resource. Several search engines exist that facilitate the searching of information from different information sources. For example, Google® and Yahoo® are both commonly used search engines that search information on the World Wide Web. When a user or consumer is seeking products, services or information, the user will enter text or a string of text relevant to the information sought. The user then receives a number of results or “hits” based on the search query.
The user is generally able to search within the first search and narrow down results by clicking an option to search within results. In addition, the user may assign certain filtering parameters to the search. The filtering may be absolute or relative. The net results are then listed, often in order of relevance by a predetermined algorithm. The user is then presented with a large number of websites in which the user must navigate and determine, of the websites, which is the most valuable to the user's interests. The sheer number of websites presented to the user is a deterrent from finding a site that is uniquely tailored to the user's interests. Moreover, search engines rarely ask any uniquely identifying information which would function to distinguish the user. Essentially, the query itself is the only distinguishing feature.
After a user purchases a product found on a website, or a consumer enters a search query using a search engine, where the usage is tracked, a subsequent visit may bring up suggestions of other products that other users have bought, who also bought the first product, or searched using the same previously submitted search queries. Currently, Amazon.com® provides a “suggestion engine”, i.e. “people who bought this book also bought that book.” While this is a great feature, there are many disadvantages. Suggestion engines will use other customer's usage habits as a base to recommend products. The suggestion is inefficient because the suggested product is based on low correlating data, i.e. the fact(s) that both had previously purchased the same product(s), or used the same search terms in a search. Without more data on both users, the probability that the user will actually benefit from the suggestion is relatively low.
The power of the suggestion becomes even more attenuated when the data used in the suggestion may have been based on a user buying for a family member or friend, an item that may have been a gag gift, resulting in a suggestion tailored for a completely different purpose. Compounding further, current suggestion engines are unable to gather the necessary data to tailor the suggestion. This is because, given the state of the art, users are unwilling to spend the time necessary to enter the appropriate data into the system.
A user using a search engine, such as Google®, is faced with other disadvantages as well. Currently, search engines allow the user the ability to enter a search query and receive results. However, if the user wanted to modify search parameters and visualize the distinctions between, and effect of, each parameter, the user would be unable to do so without having to start each search over, thus requiring the user to enter text separately, tracking each query and result.
Currently, the art does not have the ability to provide tailored information to users and functionality to searching. What is needed is a way to segment searched information so it can be categorized, such as segment like-minded interests and traits; segment products, items, places, services, websites, and people; segment information and search formats such as text, video, audio, images, and combinations of format types; segment marketing campaigns by their appeal or success; and segment individual words, terms, concepts, or categories used in a text, audio, and/or video search. Further needed are ways to compare and correlate these segments.